Overview

Dataset statistics

Number of variables14
Number of observations1878
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory205.5 KiB
Average record size in memory112.1 B

Variable types

Numeric10
Categorical2
DateTime2

Alerts

BulkCapacity is highly overall correlated with TotalCapacityHigh correlation
CCS is highly overall correlated with SessionHigh correlation
ControlledSession is highly overall correlated with Preq_maxHigh correlation
Energy is highly overall correlated with Pmax and 1 other fieldsHigh correlation
EnergyCapacity is highly overall correlated with PmaxHigh correlation
Pmax is highly overall correlated with Energy and 2 other fieldsHigh correlation
Preq_max is highly overall correlated with ControlledSession and 1 other fieldsHigh correlation
SOC_departure is highly overall correlated with StayHigh correlation
Session is highly overall correlated with CCSHigh correlation
Stay is highly overall correlated with Energy and 1 other fieldsHigh correlation
TotalCapacity is highly overall correlated with BulkCapacityHigh correlation
Session is uniformly distributedUniform
Session has unique valuesUnique

Reproduction

Analysis started2025-12-05 17:50:53.445870
Analysis finished2025-12-05 17:51:17.596655
Duration24.15 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

Session
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct1878
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean939.5
Minimum1
Maximum1878
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.8 KiB
2025-12-05T17:51:17.730149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile94.85
Q1470.25
median939.5
Q31408.75
95-th percentile1784.15
Maximum1878
Range1877
Interquartile range (IQR)938.5

Descriptive statistics

Standard deviation542.27622
Coefficient of variation (CV)0.57719662
Kurtosis-1.2
Mean939.5
Median Absolute Deviation (MAD)469.5
Skewness0
Sum1764381
Variance294063.5
MonotonicityNot monotonic
2025-12-05T17:51:17.945423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2781
 
0.1%
2951
 
0.1%
8581
 
0.1%
8071
 
0.1%
7961
 
0.1%
7141
 
0.1%
7051
 
0.1%
7021
 
0.1%
6371
 
0.1%
4261
 
0.1%
Other values (1868)1868
99.5%
ValueCountFrequency (%)
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
101
0.1%
ValueCountFrequency (%)
18781
0.1%
18771
0.1%
18761
0.1%
18751
0.1%
18741
0.1%
18731
0.1%
18721
0.1%
18711
0.1%
18701
0.1%
18691
0.1%

CCS
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
CCS1
1129 
CCS2
749 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters7512
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCCS1
2nd rowCCS1
3rd rowCCS1
4th rowCCS1
5th rowCCS1

Common Values

ValueCountFrequency (%)
CCS11129
60.1%
CCS2749
39.9%

Length

2025-12-05T17:51:18.150207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-05T17:51:18.325761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ccs11129
60.1%
ccs2749
39.9%

Most occurring characters

ValueCountFrequency (%)
C3756
50.0%
S1878
25.0%
11129
 
15.0%
2749
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)7512
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C3756
50.0%
S1878
25.0%
11129
 
15.0%
2749
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)7512
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C3756
50.0%
S1878
25.0%
11129
 
15.0%
2749
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)7512
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C3756
50.0%
S1878
25.0%
11129
 
15.0%
2749
 
10.0%

Arrival
Date

Distinct1876
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
Minimum2022-04-12 19:27:00
Maximum2023-07-04 23:03:00
Invalid dates0
Invalid dates (%)0.0%
2025-12-05T17:51:18.492540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T17:51:18.728191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1873
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
Minimum2022-04-12 19:38:00
Maximum2023-07-04 23:48:00
Invalid dates0
Invalid dates (%)0.0%
2025-12-05T17:51:18.951653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T17:51:19.183448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Stay
Real number (ℝ)

High correlation 

Distinct95
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.915868
Minimum5
Maximum144
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.8 KiB
2025-12-05T17:51:19.421788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile10
Q120
median30
Q342
95-th percentile62
Maximum144
Range139
Interquartile range (IQR)22

Descriptive statistics

Standard deviation17.580331
Coefficient of variation (CV)0.53409896
Kurtosis4.4369702
Mean32.915868
Median Absolute Deviation (MAD)11
Skewness1.426052
Sum61816
Variance309.06804
MonotonicityIncreasing
2025-12-05T17:51:19.643061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3062
 
3.3%
2959
 
3.1%
2058
 
3.1%
3153
 
2.8%
2652
 
2.8%
1749
 
2.6%
3347
 
2.5%
2546
 
2.4%
1945
 
2.4%
2445
 
2.4%
Other values (85)1362
72.5%
ValueCountFrequency (%)
59
 
0.5%
612
 
0.6%
718
1.0%
820
1.1%
921
1.1%
1027
1.4%
1123
1.2%
1232
1.7%
1341
2.2%
1425
1.3%
ValueCountFrequency (%)
1441
 
0.1%
1411
 
0.1%
1371
 
0.1%
1342
0.1%
1281
 
0.1%
1101
 
0.1%
1091
 
0.1%
1071
 
0.1%
1053
0.2%
991
 
0.1%

Energy
Real number (ℝ)

High correlation 

Distinct1846
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32184.204
Minimum1165
Maximum268863
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.8 KiB
2025-12-05T17:51:19.856532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1165
5-th percentile7958.15
Q117979.75
median29367
Q343285.25
95-th percentile60904.75
Maximum268863
Range267698
Interquartile range (IQR)25305.5

Descriptive statistics

Standard deviation19407.367
Coefficient of variation (CV)0.60300908
Kurtosis23.072834
Mean32184.204
Median Absolute Deviation (MAD)12437.5
Skewness2.6548076
Sum60441936
Variance3.7664591 × 108
MonotonicityNot monotonic
2025-12-05T17:51:20.064109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
118772
 
0.1%
194982
 
0.1%
372482
 
0.1%
351892
 
0.1%
159142
 
0.1%
244732
 
0.1%
360182
 
0.1%
269932
 
0.1%
176452
 
0.1%
379512
 
0.1%
Other values (1836)1858
98.9%
ValueCountFrequency (%)
11651
0.1%
18471
0.1%
18481
0.1%
20711
0.1%
20861
0.1%
22481
0.1%
27531
0.1%
28621
0.1%
29041
0.1%
34541
0.1%
ValueCountFrequency (%)
2688631
0.1%
2444741
0.1%
1967611
0.1%
1621351
0.1%
1487901
0.1%
1210261
0.1%
1176901
0.1%
1026031
0.1%
933551
0.1%
932151
0.1%

Pmax
Real number (ℝ)

High correlation 

Distinct1834
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102494.72
Minimum13986
Maximum174846
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.8 KiB
2025-12-05T17:51:20.276844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum13986
5-th percentile44118.75
Q165060.25
median97654.5
Q3145188
95-th percentile165378.3
Maximum174846
Range160860
Interquartile range (IQR)80127.75

Descriptive statistics

Standard deviation41905.369
Coefficient of variation (CV)0.40885392
Kurtosis-1.259748
Mean102494.72
Median Absolute Deviation (MAD)36582
Skewness0.10303282
Sum1.9248509 × 108
Variance1.7560599 × 109
MonotonicityNot monotonic
2025-12-05T17:51:20.536604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
555242
 
0.1%
1578242
 
0.1%
649622
 
0.1%
1580882
 
0.1%
495122
 
0.1%
1578332
 
0.1%
389702
 
0.1%
550952
 
0.1%
602162
 
0.1%
1317302
 
0.1%
Other values (1824)1858
98.9%
ValueCountFrequency (%)
139861
0.1%
154891
0.1%
167581
0.1%
172861
0.1%
195241
0.1%
212431
0.1%
222871
0.1%
224341
0.1%
224701
0.1%
225271
0.1%
ValueCountFrequency (%)
1748461
0.1%
1744081
0.1%
1741531
0.1%
1739761
0.1%
1736611
0.1%
1736101
0.1%
1735681
0.1%
1731211
0.1%
1730011
0.1%
1723981
0.1%

Preq_max
Real number (ℝ)

High correlation 

Distinct128
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean209408.23
Minimum24000
Maximum367872
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.8 KiB
2025-12-05T17:51:20.746855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum24000
5-th percentile57328.05
Q1123081
median191382
Q3352230
95-th percentile353265
Maximum367872
Range343872
Interquartile range (IQR)229149

Descriptive statistics

Standard deviation100409.65
Coefficient of variation (CV)0.47949237
Kurtosis-1.1169775
Mean209408.23
Median Absolute Deviation (MAD)70125
Skewness0.20320307
Sum3.9326866 × 108
Variance1.0082098 × 1010
MonotonicityNot monotonic
2025-12-05T17:51:20.952542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
353265367
 
19.5%
35223074
 
3.9%
17650572
 
3.8%
16024572
 
3.8%
19241766
 
3.5%
15921065
 
3.5%
17754055
 
2.9%
11051452
 
2.8%
22387246
 
2.4%
17941846
 
2.4%
Other values (118)963
51.3%
ValueCountFrequency (%)
240001
 
0.1%
243001
 
0.1%
297815
0.3%
300001
 
0.1%
303006
0.3%
306007
0.4%
308163
0.2%
309001
 
0.1%
312002
 
0.1%
315002
 
0.1%
ValueCountFrequency (%)
36787221
 
1.1%
36683711
 
0.6%
353265367
19.5%
35223074
 
3.9%
3134791
 
0.1%
3124444
 
0.2%
2780072
 
0.1%
2769723
 
0.2%
27592820
 
1.1%
2748932
 
0.1%

ControlledSession
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size14.8 KiB
1
1416 
0
462 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1878
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
11416
75.4%
0462
 
24.6%

Length

2025-12-05T17:51:21.151888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-05T17:51:21.259341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
11416
75.4%
0462
 
24.6%

Most occurring characters

ValueCountFrequency (%)
11416
75.4%
0462
 
24.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)1878
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11416
75.4%
0462
 
24.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1878
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11416
75.4%
0462
 
24.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1878
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11416
75.4%
0462
 
24.6%

TotalCapacity
Real number (ℝ)

High correlation 

Distinct55
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16161.736
Minimum10000
Maximum80640
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.8 KiB
2025-12-05T17:51:21.445898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile10000
Q110000
median10000
Q310000
95-th percentile68179
Maximum80640
Range70640
Interquartile range (IQR)0

Descriptive statistics

Standard deviation17267.102
Coefficient of variation (CV)1.0683941
Kurtosis6.364998
Mean16161.736
Median Absolute Deviation (MAD)0
Skewness2.7554383
Sum30351740
Variance2.9815283 × 108
MonotonicityNot monotonic
2025-12-05T17:51:21.676087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100001639
87.3%
5000079
 
4.2%
4200049
 
2.6%
8044017
 
0.9%
799009
 
0.5%
768005
 
0.3%
774004
 
0.2%
776004
 
0.2%
789004
 
0.2%
187004
 
0.2%
Other values (45)64
 
3.4%
ValueCountFrequency (%)
100001639
87.3%
187004
 
0.2%
228004
 
0.2%
280002
 
0.1%
355002
 
0.1%
4200049
 
2.6%
5000079
 
4.2%
557001
 
0.1%
642001
 
0.1%
666401
 
0.1%
ValueCountFrequency (%)
806402
 
0.1%
805401
 
0.1%
805002
 
0.1%
8044017
0.9%
803403
 
0.2%
803002
 
0.1%
802401
 
0.1%
801401
 
0.1%
799401
 
0.1%
799009
0.5%

BulkCapacity
Real number (ℝ)

High correlation 

Distinct62
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12891.472
Minimum1000
Maximum64512
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.8 KiB
2025-12-05T17:51:21.888364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile8000
Q18000
median8000
Q38000
95-th percentile51652.8
Maximum64512
Range63512
Interquartile range (IQR)0

Descriptive statistics

Standard deviation13713.327
Coefficient of variation (CV)1.0637518
Kurtosis6.4121968
Mean12891.472
Median Absolute Deviation (MAD)0
Skewness2.7587761
Sum24210185
Variance1.8805533 × 108
MonotonicityNot monotonic
2025-12-05T17:51:22.118669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80001619
86.2%
4000079
 
4.2%
3360049
 
2.6%
829917
 
0.9%
6435217
 
0.9%
639209
 
0.5%
614405
 
0.3%
619204
 
0.2%
149604
 
0.2%
182404
 
0.2%
Other values (52)71
 
3.8%
ValueCountFrequency (%)
10001
 
0.1%
40001
 
0.1%
80001619
86.2%
829917
 
0.9%
100001
 
0.1%
149604
 
0.2%
182404
 
0.2%
232402
 
0.1%
284002
 
0.1%
3360049
 
2.6%
ValueCountFrequency (%)
645122
 
0.1%
644321
 
0.1%
644002
 
0.1%
6435217
0.9%
642723
 
0.2%
642392
 
0.1%
641911
 
0.1%
641121
 
0.1%
639521
 
0.1%
639209
0.5%

SOC_arrival
Real number (ℝ)

Distinct149
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.674322
Minimum0
Maximum98
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size14.8 KiB
2025-12-05T17:51:22.340296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q119
median31
Q346
95-th percentile71
Maximum98
Range98
Interquartile range (IQR)27

Descriptive statistics

Standard deviation19.147568
Coefficient of variation (CV)0.56861034
Kurtosis-0.0028001139
Mean33.674322
Median Absolute Deviation (MAD)13.01
Skewness0.64835349
Sum63240.377
Variance366.62935
MonotonicityNot monotonic
2025-12-05T17:51:22.568496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1647
 
2.5%
2145
 
2.4%
2644
 
2.3%
2541
 
2.2%
2041
 
2.2%
2340
 
2.1%
3240
 
2.1%
3039
 
2.1%
2239
 
2.1%
2938
 
2.0%
Other values (139)1464
78.0%
ValueCountFrequency (%)
02
 
0.1%
115
0.8%
210
0.5%
310
0.5%
48
 
0.4%
516
0.9%
615
0.8%
720
1.1%
822
1.2%
8.9923
1.2%
ValueCountFrequency (%)
981
 
0.1%
972
 
0.1%
951
 
0.1%
93.991
 
0.1%
931
 
0.1%
922
 
0.1%
911
 
0.1%
881
 
0.1%
874
0.2%
865
0.3%

SOC_departure
Real number (ℝ)

High correlation 

Distinct121
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.140417
Minimum12
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.8 KiB
2025-12-05T17:51:22.779862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile41
Q170
median82.99
Q393
95-th percentile100
Maximum100
Range88
Interquartile range (IQR)23

Descriptive statistics

Standard deviation18.404431
Coefficient of variation (CV)0.23255414
Kurtosis0.47746918
Mean79.140417
Median Absolute Deviation (MAD)11
Skewness-1.0463226
Sum148625.7
Variance338.7231
MonotonicityNot monotonic
2025-12-05T17:51:22.982394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100192
 
10.2%
80146
 
7.8%
90124
 
6.6%
8985
 
4.5%
9977
 
4.1%
9544
 
2.3%
9841
 
2.2%
8441
 
2.2%
93.9940
 
2.1%
7938
 
2.0%
Other values (111)1050
55.9%
ValueCountFrequency (%)
121
 
0.1%
161
 
0.1%
171
 
0.1%
17.992
 
0.1%
191
 
0.1%
211
 
0.1%
222
 
0.1%
235
0.3%
242
 
0.1%
254
0.2%
ValueCountFrequency (%)
100192
10.2%
9977
4.1%
9841
 
2.2%
9729
 
1.5%
9622
 
1.2%
95.999500871
 
0.1%
9544
 
2.3%
948
 
0.4%
93.9940
 
2.1%
9321
 
1.1%

EnergyCapacity
Real number (ℝ)

High correlation 

Distinct1873
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69682.304
Minimum9748.0556
Maximum382028.38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.8 KiB
2025-12-05T17:51:23.178382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum9748.0556
5-th percentile29715.583
Q157187.463
median72839.347
Q380505.223
95-th percentile92032.4
Maximum382028.38
Range372280.32
Interquartile range (IQR)23317.761

Descriptive statistics

Standard deviation30210.323
Coefficient of variation (CV)0.43354369
Kurtosis53.50267
Mean69682.304
Median Absolute Deviation (MAD)9270.433
Skewness5.3439251
Sum1.3086337 × 108
Variance9.1266363 × 108
MonotonicityNot monotonic
2025-12-05T17:51:23.402786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81690.306122
 
0.1%
72310.833332
 
0.1%
88773.181822
 
0.1%
74957.878792
 
0.1%
71978.333332
 
0.1%
915041
 
0.1%
72852.469261
 
0.1%
76247.260271
 
0.1%
54316.251
 
0.1%
81334.901961
 
0.1%
Other values (1863)1863
99.2%
ValueCountFrequency (%)
9748.0555561
0.1%
9979.2147891
0.1%
103701
0.1%
10808.888891
0.1%
10863.378381
0.1%
108731
0.1%
10891.149431
0.1%
10909.677421
0.1%
10925.95961
0.1%
10934.595961
0.1%
ValueCountFrequency (%)
382028.37841
0.1%
375764.18751
0.1%
373845.91
0.1%
370886.1291
0.1%
370227.35181
0.1%
368651.26981
0.1%
366733.92861
0.1%
364053.57141
0.1%
360662.90321
0.1%
355828.69571
0.1%

Interactions

2025-12-05T17:51:15.285907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T17:50:54.209813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T17:50:57.337704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T17:50:59.801000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T17:51:02.086590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T17:51:03.963871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T17:51:05.775694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T17:51:08.138798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T17:51:09.936467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T17:51:13.193951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T17:51:15.479547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T17:50:54.399489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T17:50:57.515939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-05T17:51:04.137293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T17:51:06.118201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T17:51:08.311605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T17:51:10.100100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-05T17:50:54.567931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T17:50:57.693695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-05T17:51:02.473567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-05T17:51:06.502457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T17:51:08.698666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T17:51:11.284590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T17:51:13.985337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-05T17:51:01.043155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T17:51:02.843735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T17:51:04.698754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T17:51:06.834212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T17:51:08.880387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T17:51:11.531114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T17:51:14.278635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-05T17:51:07.207455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T17:51:09.238359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-05T17:51:09.408178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-05T17:51:07.780968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T17:51:09.577934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T17:51:12.671093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T17:51:14.959038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T17:51:16.927910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T17:50:57.154670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T17:50:59.464922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T17:51:01.917311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T17:51:03.769270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T17:51:05.597233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T17:51:07.944761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T17:51:09.758970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T17:51:12.952045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-05T17:51:15.117609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-05T17:51:23.602588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
BulkCapacityCCSControlledSessionEnergyEnergyCapacityPmaxPreq_maxSOC_arrivalSOC_departureSessionStayTotalCapacity
BulkCapacity1.0000.0880.341-0.172-0.149-0.112-0.2560.0930.020-0.106-0.0340.966
CCS0.0881.0000.1040.0190.0790.1420.3090.0000.0930.9960.1140.080
ControlledSession0.3410.1041.0000.2360.4620.4080.7470.1150.0960.1040.0740.347
Energy-0.1720.0190.2361.0000.3640.6330.316-0.4020.307-0.0290.664-0.145
EnergyCapacity-0.1490.0790.4620.3641.0000.5060.4740.111-0.113-0.047-0.010-0.111
Pmax-0.1120.1420.4080.6330.5061.0000.522-0.376-0.146-0.126-0.024-0.098
Preq_max-0.2560.3090.7470.3160.4740.5221.000-0.034-0.132-0.255-0.050-0.224
SOC_arrival0.0930.0000.115-0.4020.111-0.376-0.0341.0000.316-0.008-0.0920.076
SOC_departure0.0200.0930.0960.307-0.113-0.146-0.1320.3161.000-0.0150.6680.005
Session-0.1060.9960.104-0.029-0.047-0.126-0.255-0.008-0.0151.0000.055-0.105
Stay-0.0340.1140.0740.664-0.010-0.024-0.050-0.0920.6680.0551.000-0.015
TotalCapacity0.9660.0800.347-0.145-0.111-0.098-0.2240.0760.005-0.105-0.0151.000

Missing values

2025-12-05T17:51:17.247331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-05T17:51:17.463935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

SessionCCSArrivalDepartureStayEnergyPmaxPreq_maxControlledSessionTotalCapacityBulkCapacitySOC_arrivalSOC_departureEnergyCapacity
0278CCS12022-08-11 23:33:002022-08-11 23:37:0059632.0001683931924171791406331225.99949535.99949591504.000000
1897CCS12023-04-22 09:46:002023-04-22 09:50:0053993.000872011924171752006016045.00000049.00000094833.750000
2958CCS12023-05-18 13:24:002023-05-18 13:28:0054567.00079593252297110000800021.00000027.00000072310.833333
31046CCS12023-06-18 16:14:002023-06-18 16:18:0055591.000120198232965110000800046.99000055.00000066310.237203
41095CCS12023-06-28 14:39:002023-06-28 14:43:0056400.000128517353265110000800045.00000054.00000067555.555556
51342CCS22022-10-14 11:48:002022-10-14 11:52:0051165.000584071094790500004000098.000000100.00000055337.500000
61352CCS22022-10-16 11:56:002022-10-16 12:00:0054431.00084300352230110000800073.00000078.00000084189.000000
71450CCS22022-11-09 11:29:002022-11-09 11:33:0054641.0001139551173001500004000031.00000038.00000062985.000000
81655CCS22023-03-30 14:54:002023-03-30 14:58:0057910.825140616141900010000800077.00000079.000000375764.187500
915CCS12022-04-16 11:19:002022-04-16 11:24:00610830.000146661367872110000800037.00000048.00000093531.818182
SessionCCSArrivalDepartureStayEnergyPmaxPreq_maxControlledSessionTotalCapacityBulkCapacitySOC_arrivalSOC_departureEnergyCapacity
1868848CCS12023-04-12 19:58:002023-04-12 21:42:0010548313.01152151924171776006208040.000000100.0076495.583333
1869657CCS12023-03-01 16:42:002023-03-01 18:28:0010744563.040776121257110000800027.00000084.0074271.666667
1870783CCS12023-03-29 20:21:002023-03-29 22:09:0010954079.0992821924171776006208031.998711100.0075550.112377
1871265CCS12022-08-03 18:33:002022-08-03 20:22:0011079352.0154158275928110000800016.00000097.0093067.160494
1872368CCS12022-10-25 15:31:002022-10-25 17:38:00128244474.0149946223296110000800033.00000096.00368651.269841
1873668CCS12023-03-03 16:45:002023-03-03 18:58:0013460079.0101439275928110000800020.00000097.0074123.441558
1874778CCS12023-03-29 10:33:002023-03-29 12:46:0013430498.064902123921110000800051.00000098.0061644.893617
187561CCS12022-04-28 14:32:002022-04-28 16:48:00137268863.0150498223296110000800025.00000093.99370227.351790
1876743CCS12023-03-21 13:45:002023-03-21 16:05:0014146939.0516095680811000080006.000000100.0047438.351064
18771750CCS22023-05-14 13:38:002023-05-14 16:01:0014450409.0515525577311000080003.000000100.0049369.639175